With the continuous development of technologies such as the Internet and e-businesses, more and more image data need to be processed. Effective pre-processing of images is the foundation for subsequent tasks such as image classification and feature extraction. It is an important pre-processing measure to localize a location area of an image subject. The processing of image subject localization generally refers to identifying an object in an image, localizing the object in the image, and then determining a subject area where the object in the image is located. Therefore, interference of a background area with subsequent image processing may be reduced, thus improving the precision and performance of related image processing algorithms.
Image object localization methods under the conventional techniques mainly include a full-supervision-based object detection method and some other weak-supervision object detection algorithms. However, in actual applications of these algorithms, images need to be labeled manually, and model training with a relatively large computational load and a relatively complex process needs to be performed. For example, in the weak-supervision algorithm, image category information needs to be labeled manually. In the full-supervision object detection algorithm, in addition to category information, object bounding box data of each object in the image is further needed. Therefore, a larger amount of data needs to be labeled manually. As there are massive data at the Internet currently, the amount of image data to be processed is generally tremendous. The method of implementing image subject localization by marking tremendous image data manually cannot meet the requirement of rapid image data processing.
Therefore, a solution that may localize an object in an image more rapidly and efficiently is needed urgently in the industry.